Abstract
Hybrid many-objective cuckoo search algorithm (HMaOCS) is a newly proposed method for Many-objective optimization problems (MaOPs), and has achieved promising performance. However, Lévy and Gaussian distributions used in global search manner of HMaOCS is originally proposed for optimization problems with one objective, and they are not suitable for MaOPs as illustrated in this paper. To further exploit the potential of HMaOCS, this paper investigates four different probability distributions and their six corresponding combinations. Comparison results illustrate that the combination of Lévy and Exponential distributions is able to greatly improve HMaOCS. On the basis of comparison results and analysis on both DTLZ and WFG test suites with 2, 3, 4, 6, 8 and 10 objectives, it can be concluded that HMaOCS with Lévy and Exponential distributions exhibits better performance compared with most advanced algorithms.
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This paper is supported by National Natural Science Foundation of China under Grant Nos. 61806138, U1636220, 61663028, 71771176, 51775385, 61703279 and 71371142, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, PhD Research Startup Foundation of Taiyuan University of Science and Technology under Grant No. 20182002, the Distinguished Young Talents Plan of Jiang-xi Province under Grant No. 20171BCB23075, the Natural Science Foundation of Jiang-xi Province under Grant No. 20171BAB202035.
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Authors Zhihua Cui, Maoqing Zhang, Hui Wang, Xingjuan Cai, Wensheng Zhang, Jinjun Chen declare that they have no conflict of interest.
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Cui, Z., Zhang, M., Wang, H. et al. Hybrid many-objective cuckoo search algorithm with Lévy and exponential distributions. Memetic Comp. 12, 251–265 (2020). https://doi.org/10.1007/s12293-020-00308-3
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DOI: https://doi.org/10.1007/s12293-020-00308-3